Skip to main content
SpringerLink
Log in

Multichannel heuristic learning based single layer neural network filter for mixed noise suppression from color Doppler ultrasound images

  • Special Issue Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Mixed noise suppression from color Doppler ultrasound (CDUS) images is always a challenging task because the noise distribution usually does not have a parametric model and heavy tail. It affects the inherent features of the image awkwardly. Consequently, identifying an internal blockage or hemorrhage of the patient becomes arduous in such conditions. An acquired CDUS image is majorly affected by speckle noise and can be coupled with Gaussian and impulse noises. In this paper, the evolutionary multichannel Jaya based functional link artificial neural network (named as M-Jaya-FLANN) has been proposed to get rid of mixed noise from the CDUS images. The subjective evaluation and the measurement of qualitative metrics, such as structural similarity index, computational time, convergence rate, and Friedman’s test are carried out for the performance analysis of different filters. The research outcomes exhibit the supremacy of the proposed filter over other competitive filters and can handle real-time noise elimination after completion of training. For the experimentation purpose, CDUS image data are collected from Medanta hospital, Ranchi, India.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Loizou, C.P., Pattichis, C.S., Pantziaris, M., Tyllis, T., Nicolaides, A.: Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering. Med. Biol. Eng. Comput. 44, 414–426 (2006)

    Article  Google Scholar 

  2. Sudha, S., Suresh, G.R., Sukanesh, R.: Speckle noise reduction in ultrasound images using context-based adaptive wavelet thresholding. IETE J. Res. 55(3), 135–143 (2009)

    Article  Google Scholar 

  3. Rekha, C.K., K, M., Rao, G.V.S.: Speckle noise reduction in 3D ultrasound images—a review. In: IEEE Signal Processing and Communication Engineering Systems (SPACES), pp. 257–259 (2015)

  4. Jai Jaganath Babu, J., Florence Sudha, G.: Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation. Biomed. Signal Process. Control 23, 93–103 (2016)

    Article  Google Scholar 

  5. Zhao, H., Zeng, X., He, Z., Yu, S., Chen, B.: Improved functional link artificial neural network via convex combination for nonlinear active noise control. Appl. Soft Comput. J. 42, 351–359 (2016)

    Article  Google Scholar 

  6. Das, S.R., Mishra, D., Rout, M.: A hybridized ELM-Jaya forecasting model for currency exchange prediction. J. King Saud Univ. – Comput. Inf. Sci. 32(3), 345–366 (2020)

    Google Scholar 

  7. Jiang, J., Zhang, L., Yang, J.: Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Trans. Image Process. 23(6), 2651–2662 (2014)

    Article  MathSciNet  Google Scholar 

  8. Xiong, S., Zhou, Z., Member, S.: Neural filtering of colored noise based on Kalman filter structure. IEEE Trans. Instrum. Meas. 52(3), 742–747 (2003)

    Article  Google Scholar 

  9. Barletta, L., Magarini, M., Spalvieri, A.: Bridging the gap between Kalman filter and Wiener filter in carrier phase tracking. IEEE Photonics Technol. Lett. 25(11), 1035–1038 (2013)

    Article  Google Scholar 

  10. Li, Y., Lu, J., Wang, L., Yahagi, T., Okamoto, T.: Removing noise from radiological image using multineural network filter. IEEE Int. Conf. Indust. Technol. 2005, 1365–1370 (2005)

    Google Scholar 

  11. Yuanhua, G., Chunlun, H.: Functional link artificial neural networks filter for Gaussian noise. In: Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). pp. 1–5 (2013)

  12. Joseph, J., Jayaraman, S., Periyasamy, R., Simi, V.R.: An edge preservation index for evaluating nonlinear spatial restoration in MR images. Curr. Med. Imaging Rev. 13(1), 58–65 (2016)

    Article  Google Scholar 

  13. Alilou, V.K., Yaghmaee, F.: Application of GRNN neural network in non-texture image inpainting and restoration. Pattern Recogn. Lett. 62, 24–31 (2015)

    Article  Google Scholar 

  14. Mishra, S.K., Panda, G., Meher, S.: Chebyshev functional link artificial neural networks for denoising of image corrupted by salt and pepper noise. Int. J. Recent Trends Eng. 1(1), 413–417 (2009)

    Google Scholar 

  15. Laddi, A., Kumar, S., Sharma, S., Kumar, A.: Non-invasive jaundice detection using machine vision. IETE J. Res. 59(5), 591–596 (2013)

    Article  Google Scholar 

  16. Das, P., Neelima, A.: An overview of approaches for content-based medical image retrieval. Int. J. Multimedia Inf. Retriev. 6(4), 271–280 (2017)

    Article  Google Scholar 

  17. Carotenuto, R., Sabbi, G., Pappalardo, M.: Spatial resolution enhancement of ultrasound images using neural networks. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49(8), 1039–1049 (2002)

    Article  Google Scholar 

  18. Bhattacharyya, S., Pal, P., Bhowmick, S.: Binary image denoising using a quantum multilayer self organizing neural network. Appl. Soft Comput. 24, 717–729 (2014)

    Article  Google Scholar 

  19. Chang, Y., Chang, H.: Automatic brain MR image denoising based on texture feature-based artificial neural networks. Bio-Med. Mater. Eng. 26, 1275–1282 (2015)

    Article  Google Scholar 

  20. Ahirwal, M.K., Kumar, A., Singh, G.K.: EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms. IEEE/ACM Trans. Comput. Biol. Bioinf. 10(6), 1491–1504 (2013)

    Article  Google Scholar 

  21. Naik, B., Nayak, J., Behera, H.S.: A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data. J. King Saud Univ. Comput. Inf. Sci. 30(1), 120–139 (2016)

    Google Scholar 

  22. Montana, D.J., Davis, L.: Training feed forward neural networks using genetic algorithms. IJCAI 89, 762–767 (1989)

    MATH  Google Scholar 

  23. Bashir, Z.A.: Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Trans. Power Syst. 24(1), 20–27 (2009)

    Article  MathSciNet  Google Scholar 

  24. Kumar, M., Mishra, S.K., Sahu, S.S.: Cat swarm optimization based functional link artificial neural network filter for Gaussian noise removal from computed tomography images. Appl. Comput. Intel. Soft Comput. 2016, 1–6 (2016)

    Google Scholar 

  25. Xu, R., Ii, D.C.W., Frank, R.L.: Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Trans. Comput. Biol. Bioinf. 4(4), 681–692 (2007)

    Article  Google Scholar 

  26. Mirjalili, S., Mohd Hashim, S.Z., Moradian Sardroudi, H.: Training feed forward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput. 218(22), 11125–11137 (2012)

    MathSciNet  MATH  Google Scholar 

  27. Zhang, D., Mabu, S., Hirasawa, K.: Noise reduction using genetic algorithm based PCNN method. In: IEEE conf. Systems Man and Cybernetics (SMC). pp. 2627–2633 (2010)

  28. Saadi, S., Guessoum, A., Bettayeb, M.: ABC optimized neural network model for image deblurring with its FPGA implementation. Microprocess. Microsyst. 37(1), 52–64 (2013)

    Article  Google Scholar 

  29. Kumar, M., Mishra, S.K.: Particle swarm optimization-based functional link artificial neural network for medical image denoising. In: Computational Vision and Roboticsomputational Vision and Robotics. pp. 105–111 (2015)

  30. Kumar, M., Mishra, S.K.: Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image. Bio-Med. Mater. Eng. 28(6), 643–654 (2017)

    Article  Google Scholar 

  31. Rao, R.V., More, K.C., Taler, J., Ocłoń, P.: Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl. Therm. Eng. 103, 572–582 (2016)

    Article  Google Scholar 

  32. Suraj, S., Sinha, R.K., Ghosh, S.: Jaya based ANFIS for Monitoring of Two Class Motor Imagery Task. IEEE Access 4, 9273–9282 (2016)

    Article  Google Scholar 

  33. Kumar, M., Mishra, S.K.: Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images. Multimedia Tools Appl. 77, 24405–24425 (2018)

    Article  Google Scholar 

  34. Kumar, M., Mishra, S.K.: Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images. Biomed. Res. 28(9), 4159–4164 (2017)

    Google Scholar 

  35. Ruzon, M.: RGB2Lab. MathWorks, 2009. Available:https://in.mathworks.com/matlabcentral/fileexchange/24009-rgb2lab?focused=5114484&tab=function. (Accessed: 01 Jan 2017).

  36. Brunet, D., Vrscay, E.R., Wang, Z.: On the mathematical properties of the structural similarity index. IEEE Trans. Image Process. 21(4), 1488–1495 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Amit Kumar Singh, MD Radio-diagnosis and Dinesh Das, Radiologist, Medanta Abdurrazzaque Ansari Memorial Weavers Hospital, Ranchi, India for his expert comments and support.

Author information

Authors and Affiliations

  1. Department of Electronics and Biomedical Engineering, Mody University of Science & Technology, Sikar, 322311, India

    Manish Kumar

  2. Department of Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, 835215, India

    Sudhansu Kumar Mishra

  3. Department of Computer Science & Engineering, Indian Institute of Information Technology Bhagalpur, Bhagalpur, 813210, India

    Dilip Kumar Choubey

  4. Department of Computer Science and Engineering, Mody University of Science & Technology, Sikar, 322311, India

    Sunil Kumar Jangir

  5. Department of Computer Science and Engineering, Poornima Institute of Engineering and Technology, Jaipur, 302022, India

    Dinesh Goyal

Authors
  1. Manish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sudhansu Kumar Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dilip Kumar Choubey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sunil Kumar Jangir
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dinesh Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manish Kumar.

Ethics declarations

Conflict of interest

There is no conflict of interest between authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, M., Mishra, S.K., Choubey, D.K. et al. Multichannel heuristic learning based single layer neural network filter for mixed noise suppression from color Doppler ultrasound images. J Real-Time Image Proc 18, 1397–1408 (2021). https://doi.org/10.1007/s11554-020-01061-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-020-01061-z

Keywords

Search

Navigation